Constraint-Following Approach for Platoon Control Strategy of Connected Autonomous Vehicles
Bibliographic record
Abstract
The current platoon control strategies of connected autonomous vehicles (CAVs) focus on controlling the fixed intervehicle distance, i.e., the string stability of the platoon system. Here, we aimed to design a CAV platoon control strategy based on a constraint-following approach to solve the problem of platoon starting. As the resistance of the vehicle during driving varies with time, this study regarded the CAV platoon system as a changing dynamic system and introduced the Udwadia–Kalaba (U–K) approach to simplify the solution. Apart from adding an equality constraint, unlike most other studies, this study imposed a bilateral inequality constraint on the intervehicle distance between successive CAVs to prevent collisions. Meanwhile, a diffeomorphism method was introduced to transform the bounded state into an unbounded state. The proposed control strategy could render each CAV compliant with both the original imposed bilateral inequality constraint and the equality constraint. The former avoids collisions, and the latter indicates the string stability of the designed CAV platoon system. The effectiveness of the proposed controller was verified by numerical experiments. The gap errors tend to converge to zero, which is not amplified by the propagation of traffic flow.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".